Digital Twins of Cyber Physical Systems in Smart Manufacturing for Threat Simulation and Detection with Deep Learning for Time Series Classification

With increasing reliance on Cyber Physical Systems (CPS) for automation and control in Industry 4.0 and 5.0, ensuring their security against cyber threats has become paramount. Traditional security mechanisms, constrained by operational continuity and safety requirements, offer limited proactive threat detection capabilities against sophisticated Advanced Persistent Threats (APT). This research introduces the use of a Digital Twin testbed for repeatable simulation of diverse threat scenarios, generation of rich and varied datasets that depict a cyber incident, along with the ability to train time-series classification models for attack recognition. Our research aims to overcome the limitations of physical testbeds and challenges of data scarcity for Machine Learning (ML) or Deep Learning (DL) model development. By leveraging Digital Twins for data-driven analysis, this study proposes the use of supervised DL for accurate threat detection and classification in CPS within smart manufacturing. This paper demonstrates that Digital Twins testbed provides a cost-effective option for generating datasets to train and test supervised deep learning-based time series classification model for threat detection in CPS. It also discusses the benefits and limitations of the proposed testbed and suggests future research areas.